Advanced Electron Microscopy Techniques and Applications

Últimos artículos

• Fold'EM: Direct atomic structure inference from Cryo-EM particles

  Advaith Maddipatla, M\"art-Erik M\"aeots, Marco Pegoraro, Nikolaus Dr\"ager, Roberto Covino, Sanketh Vedula, Martin Pacesa, Alex M. Bronstein · 2 de octubre de 2026

  Single-particle cryo-electron microscopy (cryo-EM) has become a widely adopted technique for biomolecular structure determination. The conventional cryo-EM computational pipeline first combines many particle images to reconstruct an electrostatic potential (ESP) map and then fits an atomic model to …

• Universal Drift Correction for Multidimensional Scanning Microscopy

  Sangjoon Lee, William Millsaps, Dasol Yoon, Caitlyn Obrero, Guoliang Hu, Corrie Barnes, Cedric Lim, Andrew Barnum, Arthur R. C. McCray, Colin Ophus · 28 de septiembre de 2026

  In scanning microscopy, drift causes the specimen to be sampled at positions displaced from the nominal probe positions. This displacement alters the spatial assignment of the recorded signals and biases quantitative measurements across two-dimensional imaging, channel-resolved spectroscopic mapping…

• Structure-Guided Masked Autoencoders for Ultra-High Resolution Scientific Image Understanding

  Enzhi Zhang, Du Wu, Rui Zhong, Cong Ma, Isaac Lyngaas, Amir Koushyar Ziabari, Xiao Wang, Peng Chen, Tao Luo, Toshio Endo, Fumiyoshi Shoji, Kento Sato, Kentaro Uesugi, Takayuki Nonoyama, Ryuji Kiyama, Masahiro Yoshida, Masaru Tezuka, Tetsuya Ishikawa, Satoshi Matsuoka, Masaharu Munetomo, Mohamed Wahib · 28 de septiembre de 2026

  Self-supervised pre-training with Vision Transformers, including Masked Autoencoders (MAE), is difficult to apply to gigapixel scientific images. Random masking is poorly matched to the structured, multi-scale morphology of scientific data, while uniform tokenization produces prohibitively long sequ…

• Atelier: Learning Local Self-Supervised Features for CryoEM Volumes via Hypernetworks

  Phillip Lo, Sudarshan Babu, Dari Kimanius, Aly A. Khan · 28 de septiembre de 2026

  CryoEM map interpretation requires features that are spatially localized, consistent across samples, and informative across spatial scales. Most deep learning methods for map annotation extract features from fixed voxel grids. However, implicit neural representations (INRs) are able to model volumet…

• A benchmark dataset and baseline methods for four-dimensional STEM diffraction patterns

  Yuyan Guan, Haoran Zhang, Zian Mao, Antong Yang, Caifei Li, Jialong Wang, Chuying Ouyang, Hong Wang, Xiaoqin Zeng, Yujun Xie · 21 de septiembre de 2026

  Four-dimensional scanning transmission electron microscopy (4D-STEM) records a two-dimensional diffraction pattern at each electron-probe position, yielding spatially resolved reciprocal-space information but large, heterogeneous data volumes. Here we describe 4D-ImageNet, a collection of 174,000 di…

• Symmetry-aware super-resolution of crystal orientation maps via invariant latent-space learning

  Umang Garg, Warren Zamudio, McLean P. Echlin, Samantha H. Daly, Tresa M. Pollock, B. S. Manjunath · 11 de septiembre de 2026

  Crystal-orientation maps are physical fields defined only up to crystal symmetry; electron backscatter diffraction (EBSD) resolves them experimentally, but acquisition-time constraints limit spatial resolution. Unlike conventional images, EBSD data lie on the quotient space $\mathrm{SO}(3)/G$, where…

• FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy

  Tingyin Zhao, Mingtao Huang, Yuan Shen · 9 de septiembre de 2026

  Automating filament tracing in Cryo-Electron Microscopy (Cryo-EM) is essential for 3D helical reconstruction but challenged by intersecting topologies and extremely low Signal-to-Noise Ratios ($\text{SNR} = \sigma_s^2/\sigma_n^2$ < 0.1 or -10 dB). Existing paradigms fail: pixel-wise segmenters suffe…

• The microscope is the mask: privileged views and labels from a cryo-ET forward model

  Bogdan Toader, Kiarash Jamali, Tanmay A. M. Bharat, Sjors H. W. Scheres · 7 de septiembre de 2026

  We explore the use of simulated data for training a model for protein annotation in crowded cryo-electron tomography volumes reconstructed from images collected at limited tilt angles and severely corrupted by the measurement operator. Firstly, we leverage the corruptions imposed by the forward mode…

• A meta-algorithm for ab initio reconstruction of complex mixtures in cryo-EM

  Alkin Kaz, Arda Kaz, Ellen D. Zhong · 27 de agosto de 2026

  We describe a systematic approach for spawning and aggregating multi-class cryo-EM reconstruction jobs. This approach formalizes standard ad hoc strategies of iterative classification and filtering typically used by practitioners to sort impure, heterogeneous samples. To our knowledge, this is the f…

• Composition-Driven Phase Evolution in Sm-Doped BiFeO3 via Latent-Field Reconstruction of Atomically Resolved STEM Data

  Newsha Javanmardi, Christopher T. Nelson, Anna N. Morozovska, Eugene A. Eliseev, Ichiro Takeuchi, Sergei V. Kalinin · 21 de agosto de 2026

  Functionalities of ferroelectric materials are governed by the spatial organization and coupling of polarization, strain, lattice rotation, and structural order accessible via atomically resolved scanning transmission electron microscopy (STEM) images. Quantitative interpretation of atomic-resolutio…

• An Interactive, Automated 4D-STEM data acquisition and analysis routine for Scanning Electron Nanobeam Diffraction and Ptychography experiments

  Mohsen Danaie, Max England, Yiming Xu, Ruomu Zhang, Ed Darnbrough, Josh Willem De Boer, Frederick Allars, Zaeem Najeeb, Aakash Varambhia, Jinseok Ryu, Benjamin Bradnick, Damien McGrouther, Manfred E. Schuster, Christopher S. Allen · 17 de agosto de 2026

  Modern transmission electron microscopes are versatile instruments which have become indispensable tools for understanding structure and chemical composition at the nano- and atomic scale. In the physical sciences these instruments are still largely manually controlled, requiring significant operato…

• NanoMorph-3D: An End-to-End Physics-Driven Unrolling Framework for Nanomaterial Reconstruction

  Beiyuan Zhang, Hesong Li, Ziqi Wu, Ruiwen Shao, Ying Fu · 5 de agosto de 2026

  Precise 3D characterization of nanomaterials is essential for unlocking structure-property relationships. However, standard electron tomography is fundamentally limited by the missing wedge problem. Consequently, conventional algorithms suffer from severe geometric distortions, a challenge further c…

• Physics-Aligned Self-Supervised Learning for Scientific Imaging

  Bashir Kazimi, Stefan Sandfeld · 3 de agosto de 2026

  Data augmentations define the invariances learned by self-supervised learning (SSL). Standard augmentation pipelines were designed for natural images, yet scientific imaging modalities are governed by physical measurement processes with distinct symmetry and acquisition constraints. Enforcing invari…

• Frequency-Aware Dual-Stream Learning for Balanced Realism and Fidelity in Electron Microscopy Imaging

  Longmi Gao, Zhengkai Zhao, Pan Gao, Manoranjan Paul · 27 de julio de 2026

  Electron microscopy enables nanoscale cellular visualization but faces a trade-off between imaging resolution and acquisition speed. Existing learning-based methods rely on single-stream architectures that struggle to balance perceptual realism and quantitative fidelity, either over-smoothing detail…

• Unpaired Joint Distribution Modeling via Multi-Scale Image Representations

  Yihang Zou, Hui Zhang, Zuowei Shen, Chenglong Bao · 10 de julio de 2026

  This paper studies the problem of learning a joint distribution from marginal observations, which is inherently ill-posed due to the ambiguity of feasible couplings. We propose LUD-MSR, a latent-variable probabilistic framework that models the joint distribution via auxiliary representations and opt…

• Probe-EM: Targeted Neuron Tracing via Training-Free Semantic Verification

  Liuyun Jiang, Yanchao Zhang, Jinyue Guo, Chuanyue Chen, Haiyang Yan, Ye Yuan, Jing Liu, Hua Han · 7 de julio de 2026

  Establishing large-scale, high-resolution neural connectivity maps is fundamental to elucidating the structural basis of brain function. However, when processing terabyte- or petabyte-scale electron microscopy data, over-segmentation inherent in automated reconstruction algorithms remains a critical…

• CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM

  Minzhang Li, Mingrui Li, Weichen Qin, Qihe Chen, Sixian Shen, Yuan Pei, Jiakai Zhang, Jingyi Yu · 1 de julio de 2026

  Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity. Current solvers are often limited to static predictions or require computationally intensive heuristic searches. We present CryoACE, an end-to-end f…

• STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy

  Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban · 30 de junio de 2026

  A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition. We present evidence to the contrary in scanning transmission electron microscopy (STEM), an atomic-resolution ima…

• High-Fidelity Synthetic Transmission Electron Microscopy Image Generation Using Diffusion Probabilistic Models for Data-Limited Semiconductor Metrology

  Johannes Boehm, Bappaditya Dey · 24 de junio de 2026

  Advanced semiconductor nodes drastically increased demand for Transmission Electron Microscopy (TEM), yet destructive sample preparation, slow imaging and high costs severely limit the availability of diverse datasets needed for downstream machine learning (ML). Synthetic data generation is becoming…

• $μ$Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM

  Marei Freitag, Olesia Korchevaia, Luca Freckmann, Anwai Archit, Constantin Pape · 22 de junio de 2026

  Vision foundation models have substantially advanced computer vision, enabling state-of-the-art performance in zero- and few-shot settings. They have been successfully applied to biomedical imaging tasks ranging from organ segmentation in computed tomography to cell segmentation in light microscopy.…

• Stitching and dimensionality effects on large artificially generated volume datasets

  Lucas von Chamier, Jan Philipp Albrecht, Dagmar Kainmüller · 19 de junio de 2026

  Generating large images via deep learning requires patching input data to accommodate hardware memory limitations, then assembling output patches, a process that can introduce stitching artifacts when neighboring patches do not align at borders. While these artifacts are known to affect segmentation…

• CRIS: Cross-Plane Self-Supervised Isotropic Restoration for Anisotropic Volumetric Imaging Across Modalities

  Adi Ahituv, Anat Ilivitzki, Moti Freiman · 16 de junio de 2026

  Anisotropic volumetric acquisitions are common in clinical MRI and volume electron microscopy (vEM), where sparse through-plane sampling creates thick slices or sections that degrade orthogonal reformats and downstream analysis. We present CRIS, a cross-plane self-supervised framework for isotropic …

• POPSICLE: Benchmark Datasets for Segmentation and Localization in CryoET

  Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens, Zhuowen Zhao, Ariana Peck, Gus L. W. Hart, Grant J. Jensen, Bridget Carragher, Dari Kimanius · 10 de junio de 2026

  Cryo-electron tomography (cryoET) has emerged as a powerful tool in structural and cellular biology by enabling direct visualization of macromolecular structures within intact cells, thereby linking molecular architecture to cellular organization in a native context. Realizing the full potential of …

• Context-Aware Deep Learning for Defect Classification in Atomic-Resolution STEM

  Jiadong Dan, Cheng Zhang, Leyi Loh, Ivan Verzhbitskiy, Yuan Chen, Goki Eda, Michel Bosman, N. Duane Loh · 9 de junio de 2026

  Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation. This limitation makes defect classification inherently ambiguous, as sim…

• Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation

  Ni Li, Nuohao Liu, Ryan Jacobs, Ajay Annamareddy, Maciej P. Polak, Kevin Field, Izabela Szlufarska, Dane Morgan · 2 de junio de 2026

  Analyzing microstructural defects in transmission electron microscopy (TEM) images, particularly in irradiated metal alloys, is often limited by the availability of high-quality, labeled data. To address this, we introduce a generative data augmentation approach using a mask-conditioned latent diffu…